14 citations · 50 across the 7 of their papers we have counts for
5 papers · 1 filter
Half-Space Proximal Stochastic Gradient Method for Group-Sparsity Regularized Problem
Tianyi Chen, Guanyi Wang, Tianyu Ding +3
Optimizing with group sparsity is significant in enhancing model interpretability in machining learning applications, e.g., feature selection, compressed sensing and model compress…
Orthant Based Proximal Stochastic Gradient Method for -Regularized Optimization
Tianyi Chen, Tianyu Ding, Bo Ji +6
Sparsity-inducing regularization problems are ubiquitous in machine learning applications, ranging from feature selection to model compression. In this paper, we present a novel st…
LASG: Lazily Aggregated Stochastic Gradients for Communication-Efficient Distributed Learning
Tianyi Chen, Yuejiao Sun, Wotao Yin
This paper targets solving distributed machine learning problems such as federated learning in a communication-efficient fashion. A class of new stochastic gradient descent (SGD) a…
Decentralized Markov Chain Gradient Descent
Tao Sun, Dongsheng Li
Decentralized stochastic gradient method emerges as a promising solution for solving large-scale machine learning problems. This paper studies the decentralized Markov chain gradie…
A Reduced-Space Algorithm for Minimizing -Regularized Convex Functions
Tianyi Chen, Frank E. Curtis, Daniel P. Robinson
We present a new method for minimizing the sum of a differentiable convex function and an -norm regularizer. The main features of the new method include: an evolving…